Overview of dermatologic problems following liver transplantation

Liver transplantation recipients, like other solid organ transplantation recipients, have an increased risk of dermatologic problems due to their long-term immunosuppression and benefit from pre-and post-transplantation screenings, and management by a dermatologist and dermatologic care should be integrated into the comprehensive, multidisciplinary care of liver transplantation recipients [1,2]. Cutaneous findings include aesthetic alterations, infections, precancerous lesions, and malignancies. The severity of skin alterations ranges from benign, unpleasant changes to life-threatening conditions [3-5]. In addition to skin cancer diagnosis and management, visits with a dermatologist serve to educate and improve the patient's sun-protection behavior. Among all solid organ transplantations, liver transplantation requires the least amount of immunosuppression, sometimes even permitting its complete cessation [6]. As a result, patients who have undergone liver transplantation tend to have fewer dermatologic complications compared with other solid organ transplantation recipients [7]. However, due to the large volume of the liver, patients undergoing liver transplantation receive more donor lymphocytes than kidney, heart, or lung transplantation recipients. Because of the immunosuppression, the transplanted lymphocytes proliferate and rarely trigger graft-versus-host-disease [8,9]. This topic will provide an overview of dermatologic disorders that may be seen following liver transplantation. A detailed discussion of skin cancer following solid organ transplantation and the general management of patients following liver transplantation are discussed separately. (See "Development of malignancy following solid organ transplantation" and "Management of skin cancer in solid organ transplant recipients" and "Long-term management of adult liver transplant recipients".)

Detecting outlying studies in meta-regression models using a forward search algorithm.

When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.